Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/150276
Authors: 
Yildiz, Muhamet
Acemoglu, Daron
Chernozhukov, Victor
Year of Publication: 
2016
Citation: 
[Journal:] Theoretical Economics [ISSN:] 1555-7561 [Volume:] 11 [Year:] 2016 [Issue:] 1 [Pages:] 187-225
Abstract: 
Under the assumption that individuals know the conditional distributions of signals given the payoff-relevant parameters, existing results conclude that as individuals observe infinitely many signals, their beliefs about the parameters will eventually merge. We first show that these results are fragile when individuals are uncertain about the signal distributions: given any such model, vanishingly small individual uncertainty about the signal distributions can lead to substantial (non-vanishing) differences in asymptotic beliefs. Under a uniform convergence assumption, we then characterize the conditions under which a small amount of uncertainty leads to significant asymptotic disagreement.
Subjects: 
Asymptotic disagreement
Bayesian learning
merging of opinions
JEL: 
C11
C72
D83
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/3.0/
Document Type: 
Article

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